I am using caret packages to compare different models.
After training a model, how to find the ROC area.
# Split data
a<- createDataPartition(data$target, p = .8, list = FALSE)
train <- data[ a,]
test <- data[-a,]
myControl = trainControl(
method = "cv",
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = FALSE,
)
model_knn = train(
target ~ .,
train,
method = "knn",
metric = "ROC",
tuneLength = 10,
trControl = myControl)
For example, this is one of the models built. If I do the following, I can get the ROC curve of my training set. But to get the ROC of my testing data set?
model_knn
plot(model)
As you have not provided any data, I am using Sonar data. You can use the following code to make ROC plot for test data
library(caret)
library(MLeval)
data(Sonar)
# Split data
a <- createDataPartition(Sonar$Class, p=0.8, list=FALSE)
train <- Sonar[ a, ]
test <- Sonar[ -a, ]
myControl = trainControl(
method = "cv",
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = FALSE,
)
model_knn = train(
Class ~ .,
train,
method = "knn",
metric = "ROC",
tuneLength = 10,
trControl = myControl)
pred <- predict(model_knn, newdata=test, type="prob")
ROC <- evalm(data.frame(pred, test$Class, Group = "KNN"))
Related
According to Applied Predictive Modeling pg. 82, createFolds() is for k-fold cross validation. I noticed if I do not specify a method in the trainControl() argument, the model output says bootstrapping was used. I demonstrate this below
library(liver)
library(caret)
library(dplyr)
data(churn)
head(churn)
churn_data <- churn
# minority = yes; majority = no
churn_data %>% group_by(churn) %>% count()
set.seed(1994)
train.index <- createDataPartition(churn_data$churn, p = 0.8, list = FALSE)
train_churn <- churn_data[train.index,]
test_churn <- churn_data[-train.index,]
myFolds <- createFolds(train_churn$churn, k = 5)
myControl <- trainControl(
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = TRUE,
savePredictions = TRUE,
index = myFolds,
sampling = "up")
set.seed(1994)
model_rf <- train(churn ~ .,
data = train_churn,
method = "rf",
ntree = 100,
tuneLength = 10,
metric = "ROC",
maximize = TRUE,
trControl = myControl)
model_rf
If I specify method = "cv", I get the same model output. This makes me think that createFolds() overrides the method in the trainControl() object. Is this true?
myControl <- trainControl(
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = TRUE,
savePredictions = TRUE,
index = myFolds,
method = "cv",
sampling = "up")
set.seed(1994)
model_rf <- train(churn ~ .,
data = train_churn,
method = "rf",
ntree = 100,
tuneLength = 10,
metric = "ROC",
maximize = TRUE,
trControl = myControl)
model_rf
Something is wrong; all the Accuracy metric values are missing:
getting this error while applying k-nn on iris data.
''' iris.knn<- iris
# Dividing data into test_train
set.seed(532)
sample.iris.knn <- sample.split(iris.knn, SplitRatio = 0.8)
train.iris.knn <- subset(iris.knn, sample.iris.knn== TRUE)
test.iris.knn <- subset(iris.knn, sample.iris.knn == FALSE)
dim(train.iris.knn)
str(train.iris.knn)
head(train.iris.knn)
# fitting K-nn model
set.seed(8237)
trControl.iris.knn <- trainControl(method = "repeatedcv",
number = 10,
repeats = 3)
iris.knn.model <- train(Species ~., data = train.iris.knn,
method = 'knn',
trainControl = trControl.iris.knn,
preProcess = c("center", "scale"),
tuneLength = 13)
# Model check
iris.knn.model
'''
There is no argument in the function train named trainControl , it is trControl so change it will solve your problem
iris.knn.model <- train(Species ~., data = train.iris.knn,
method = 'knn',
trControl = trControl.iris.knn,
preProcess = c("center", "scale"),
tuneLength = 13)
I am trying to perform a multinomial classifier. It seems to work and I am able to generate a plot with minimized logLoss vs boosting iterations, however I am having trouble extracting the error value. This is the error when I run the mnLogLoss function.
Error in mnLogLoss(predicted, lev = predicted$label) :
'data' should have columns consistent with 'lev'
data has been partitioned into.
-training
-testing
-in both, the column "label" contains the ground truth
library(MLmetrics)
fitControl <- trainControl(method = "repeatedcv", number=10, repeats=3, verboseIter = FALSE,
savePredictions = TRUE, classProbs = TRUE, summaryFunction= mnLogLoss)
gbmGrid1 <- expand.grid(.interaction.depth = (1:3), .n.trees = (1:10)*20, .shrinkage = 0.01, .n.minobsinnode = 3)
system.time(
gbmFit1 <- train(label~., data = training, method = "gbm", trControl=fitControl,
verbose = 1, metric = "logLoss", tuneGrid = gbmGrid1)
)
gbmPredictions <- predict(gbmFit1, testing)
predicted <- cbind(gbmPredictions, testing)
mnLogLoss(predicted, lev = levels(predicted$label))
For mnLogLoss, it says in the vignette:
data: a data frame with columns ‘obs’ and ‘pred’ for the observed
and predicted outcomes. For metrics that rely on class
probabilities, such as ‘twoClassSummary’, columns should also
include predicted probabilities for each class. See the
‘classProbs’ argument to ‘trainControl’.
So it's not asking for the training data. The data parameter here is just an input, so i use some simulated data:
library(caret)
df = data.frame(label=factor(sample(c("a","b"),100,replace=TRUE)),
matrix(runif(500),ncol=50))
training = df[1:50,]
testing = df[1:50,]
fitControl <- trainControl(method = "repeatedcv", number=10, repeats=3, verboseIter = FALSE,
savePredictions = TRUE, classProbs = TRUE, summaryFunction= mnLogLoss)
gbmGrid1 <- expand.grid(.interaction.depth = (1:3), .n.trees = (1:10)*20, .shrinkage = 0.01, .n.minobsinnode = 3)
gbmFit1 <- train(label~., data = training, method = "gbm", trControl=fitControl,verbose = 1, metric = "logLoss", tuneGrid = gbmGrid1)
)
And we put together obs, pred and the last two columns are probabilities of each class:
predicted <- data.frame(obs=testing$label,
pred=predict(gbmFit1, testing),
predict(gbmFit1, testing,type="prob"))
head(predicted)
obs pred a b
1 b a 0.5506054 0.4493946
2 b a 0.5107631 0.4892369
3 a b 0.4859799 0.5140201
4 b a 0.5090264 0.4909736
5 b b 0.4545746 0.5454254
6 a a 0.6211514 0.3788486
mnLogLoss(predicted, lev = levels(predicted$obs))
logLoss
0.6377392
I'm trying to use log loss as loss function for training with Caret, using the data from the Kobe Bryant shot selection competition of Kaggle.
This is my script:
library(caret)
data <- read.csv("./data.csv")
data$shot_made_flag <- factor(data$shot_made_flag)
data$team_id <- NULL
data$team_name <- NULL
train_data_kaggle <- data[!is.na(data$shot_made_flag),]
test_data_kaggle <- data[is.na(data$shot_made_flag),]
inTrain <- createDataPartition(y=train_data_kaggle$shot_made_flag,p=.8,list=FALSE)
train <- train_data_kaggle[inTrain,]
test <- train_data_kaggle[-inTrain,]
folds <- createFolds(train$shot_made_flag, k = 10)
ctrl <- trainControl(method = "repeatedcv", index = folds, repeats = 3, summaryFunction = mnLogLoss)
res <- train(shot_made_flag~., data = train, method = "gbm", preProc = c("zv", "center", "scale"), trControl = ctrl, metric = "logLoss", verbose = FALSE)
And this is the traceback of the error:
7: stop("'data' should have columns consistent with 'lev'")
6: ctrl$summaryFunction(testOutput, lev, method)
5: evalSummaryFunction(y, wts = weights, ctrl = trControl, lev = classLevels,
metric = metric, method = method)
4: train.default(x, y, weights = w, ...)
3: train(x, y, weights = w, ...)
2: train.formula(shot_made_flag ~ ., data = train, method = "gbm",
preProc = c("zv", "center", "scale"), trControl = ctrl, metric = "logLoss",
verbose = FALSE)
1: train(shot_made_flag ~ ., data = train, method = "gbm", preProc = c("zv",
"center", "scale"), trControl = ctrl, metric = "logLoss",
verbose = FALSE)
When I use defaultFunction as summaryFunction and no metric specified in train, it works, but it doesn't with mnLogLoss. I'm guessing it is expecting the data in a different format than what I am passing, but I can't find where the error is.
From the help file for defaultSummary:
To use twoClassSummary and/or mnLogLoss, the classProbs argument of trainControl should be TRUE. multiClassSummary can be used without class probabilities but some statistics (e.g. overall log loss and the average of per-class area under the ROC curves) will not be in the result set.
Therefore, I think you need to change your trainControl() to the following:
ctrl <- trainControl(method = "repeatedcv", index = folds, repeats = 3, summaryFunction = mnLogLoss, classProbs = TRUE)
If you do this and run your code you will get the following error:
Error: At least one of the class levels is not a valid R variable name; This will cause errors when class probabilities are generated because the variables names will be converted to X0, X1 . Please use factor levels that can be used as valid R variable names (see ?make.names for help).
You just need to change the 0/1 levels of shot_made_flag to something that can be a valid R variable name:
data$shot_made_flag <- ifelse(data$shot_made_flag == 0, "miss", "made")
With the above changes your code will look like this:
library(caret)
data <- read.csv("./data.csv")
data$shot_made_flag <- ifelse(data$shot_made_flag == 0, "miss", "made")
data$shot_made_flag <- factor(data$shot_made_flag)
data$team_id <- NULL
data$team_name <- NULL
train_data_kaggle <- data[!is.na(data$shot_made_flag),]
test_data_kaggle <- data[is.na(data$shot_made_flag),]
inTrain <- createDataPartition(y=train_data_kaggle$shot_made_flag,p=.8,list=FALSE)
train <- train_data_kaggle[inTrain,]
test <- train_data_kaggle[-inTrain,]
folds <- createFolds(train$shot_made_flag, k = 3)
ctrl <- trainControl(method = "repeatedcv", classProbs = TRUE, index = folds, repeats = 3, summaryFunction = mnLogLoss)
res <- train(shot_made_flag~., data = train, method = "gbm", preProc = c("zv", "center", "scale"), trControl = ctrl, metric = "logLoss", verbose = FALSE)
I am trying to combine signals from different models using the example described here . I have different datasets which predicts the same output. However, when I combine the model output in caretList, and ensemble the signals, it gives an error
Error in check_bestpreds_resamples(modelLibrary) :
Component models do not have the same re-sampling strategies
Here is the reproducible example
library(caret)
library(caretEnsemble)
df1 <-
data.frame(x1 = rnorm(200),
x2 = rnorm(200),
y = as.factor(sample(c("Jack", "Jill"), 200, replace = T)))
df2 <-
data.frame(z1 = rnorm(400),
z2 = rnorm(400),
y = as.factor(sample(c("Jack", "Jill"), 400, replace = T)))
library(caret)
check_1 <- train( x = df1[,1:2],y = df1[,3],
method = "nnet",
tuneLength = 10,
trControl = trainControl(method = "cv",
classProbs = TRUE,
savePredictions = T))
check_2 <- train( x = df2[,1:2],y = df2[,3] ,
method = "nnet",
preProcess = c("center", "scale"),
tuneLength = 10,
trControl = trainControl(method = "cv",
classProbs = TRUE,
savePredictions = T))
combine <- c(check_1, check_2)
ens <- caretEnsemble(combine)
First of all, you are trying to combine 2 models trained on different training data sets. That is not going to work. All ensemble models will need to be based on the same training set. You will have different sets of resamples in each trained model. Hence your current error.
Also building your models without using caretList is dangerous because you will have a big change of getting different resample strategies. You can control that a bit better by using the index in trainControl (see vignette).
If you use 1 dataset you can use the following code:
ctrl <- trainControl(method = "cv",
number = 5,
classProbs = TRUE,
savePredictions = "final")
set.seed(1324)
# will generate the following warning:
# indexes not defined in trControl. Attempting to set them ourselves, so
# each model in the ensemble will have the same resampling indexes.
models <- caretList(x = df1[,1:2],
y = df1[,3] ,
trControl = ctrl,
tuneList = list(
check_1 = caretModelSpec(method = "nnet", tuneLength = 10),
check_2 = caretModelSpec(method = "nnet", tuneLength = 10, preProcess = c("center", "scale"))
))
ens <- caretEnsemble(models)
A glm ensemble of 2 base models: nnet, nnet
Ensemble results:
Generalized Linear Model
200 samples
2 predictor
2 classes: 'Jack', 'Jill'
No pre-processing
Resampling: Bootstrapped (25 reps)
Summary of sample sizes: 200, 200, 200, 200, 200, 200, ...
Resampling results:
Accuracy Kappa
0.5249231 0.04164767
Also read this guide on different ensemble strategies.